In the latest conducted poll of more than 200 SEO professionals and digital marketers, and one question was asked who uses SEO tactics and insights in the organizations. The answer was, in general, every content or demand generation marketing discipline uses SEO insights to some extent in their work.
This makes brands are placing greater demands on SEO, SEO-related content development, and the experts who handle it. And as a proper outcome, AI SEO is becoming a need in digital marketing; the poll clearly brings out that “AI is capable of extracting insights from data at scale.”
In the contemporary world, the technology is ideal for SEO, which creates a large amount of data and is driven by insights. AI not only makes it easier to extract greater insights from search data, but it also makes it possible to do it in real-time. Everything appears to be in order. But how can webmasters begin to comprehend and implement ideas with AI?
With our system dependency, we eventually started at Marketing AI Institute researching artificial intelligence applications in a variety of digital marketing disciplines, including AI SEO. We sometimes use artificial intelligence in our own advertising and keep track of tens of millions of virtual reality providers. But we’ve also discovered several practical ways for marketers to get started using AI SEO tools right away. To grasp the fundamentals of AI, you don’t need to be a rocket scientist. Learn: Optimize Your SEO Techniques By Using Best WordPress SEO Plugin
You’ll probably get different replies if you ask experts what artificial intelligence is. However, the spokesperson of a Google-acquired AI business offers a simple explanation that we like.
We can educate machines to behave in human-like ways. We can empower them in seeing, hearing, communicate, act, and publish.
If such machines could educate themselves to get better at viewing, listening, talking, walking, and composing even without the help of humans, they’d be termed AI.
That is precisely where we are with AI right now.
Alexa, a voice assistant, is an excellent illustration of this.
Let’s imagine you ask Alexa to “play my Spotify exercise playlist.”
Alexa listens to what you say, interprets it, and then replies and acts. At no time does Alexa receive direct instructions from a human on how to interpret your speech, respond, or take action.
Alexa provides the functionality to guess what your words mean, how to reply to them, and what action you’re demanding all on its own.
Alexa isn’t expressly designed to improve with each user’s inquiry. Instead, Alexa collects data from each encounter with a consumer in order to better the next one.
As a result, Alexa’s query answers improved by 12 percentage points (to 73 percent accuracy) in December 2018 compared to July of that year.
So, how does this differ from standard software?
To improve, traditional software must be explicitly written.
Alexa would not exist as a consumer product if it were not powered by AI. If you asked Alexa to play your workout music and she didn’t understand, you’d have to correct it manually before it could improve and get it right the next time.
With millions of Alexa users issuing millions of commands in real-time, this is unfeasible.
Alexa, on the other hand, can utilize AI to learn from data at scale and then enhance a response. Learn– How to install Amazon Alexa on a Windows PC
How Artificial Intelligence (AI) Is Being Used in SEO Today
Artificial intelligence is now used in practically every search engine on the planet for a variety of purposes in SEO.
Voice and Text Search
Artificial intelligence is used by your preferred search engine to give relevant results for your query. To process a search query, search engines use advanced AI, machine learning, and deep learning, and then anticipate which results will fulfill any given search. Search engines don’t publish exactly how their AI systems function, but they do give hints, as any SEO expert who follows Google algorithm revisions knows.
That implies, whether you like AI or not, it has a significant impact on how your content ranks and how your business is discovered, regardless of which search engine you use. It also determines how search engines interpret and categories your website’s content.
The link between voice search and AI is too important to overlook. To function, voice search relies on AI technologies such as natural language synthesis and processing. And the use of voice is just increasing
We clearly know that voice device are abundant and can be found in our homes, automobiles, and smartphones slowly in SEO too.
The competition for voice continues, and organizations must reconsider their web presence or even how they communicate with customers. New realities emerge as a result of this technology.
Content optimization, keyword research, and topic discovery
It’s more than half the battle to figure out what queries, terms, or phrases to optimize for. Artificial intelligence software can assist. AI is particularly good at discovering patterns in massive collections of data, such as search volume statistics.
In reality, technologies like these may propose topics for you to write about in order to dominate search traffic for specific keyword clusters. They employ artificial intelligence to extract subjects from search data so you can see what other top-ranking sites are doing to rank first for any given query.
It’s one thing to create new material based on search results. However, AI may also assist you in optimizing your existing content so that it ranks higher in search results. It’ll make recommendations for how to improve existing material so that it ranks higher for specific terms. In either scenario, AI can play a significant role in your content strategy.
Overall, these techniques demonstrate a fundamental reality about artificial intelligence:
There are a lot of manual things that marketers undertake every day that people aren’t really good at, don’t enjoy doing, and that a computer can do significantly better and at scales, such as subject discovery and keyword research. Hence, later or sooner AI will increase the competition proportion to rank on Seach engines and content creation to another level.
Published at Sun, 25 Jul 2021 14:48:45 +0000
There is no doubt of the importance of data powering the most advanced applications in use today, and especially artificial intelligence and machine learning applications that are so dependent on good quality, relevant data. Indeed, the foundations of best practices AI methodologies, the CPMAI methodology in particular, requires a foundation of understanding the necessary data for an AI project and preparing that data for use.
Reinforcing the importance of the role of data in AI and advanced analytic systems, Rick McFarland, Chief Data Officer – LexisNexis Legal & Professional shares his insights at an upcoming Data for AI virtual event on August 19. In an interview with Forbes, he shares some perspectives on the role data takes for AI projects at his organization.
Q: What are some innovative ways you’re leveraging advanced data analytics to benefit LexisNexis?
Rick McFarland: As the Chief Data Officer at LexisNexis Legal & Professional, I think one of the innovative and important ways we leverage advanced data analytics is by increasing the utilization and the quality of our Data. For example, most of our data is text-based (e.g. legal briefs, pleadings, motions, case law, dockets, and other documents). Dealing with this type of textual data in its raw state is challenging, complex, and nuanced. That’s why many data scientists and analysts steer clear of it. However, by transforming this data into embedding vectors by using advanced NLP methods like BERT, we create an entirely new data asset that can be leveraged by data scientists and analysts across the organization to create models and features for our products. Think of the embedding vector transformation as a conversion of the text data into numerical data. For example, every word and sentence can be represented by a numerical vector; think a row of numbers in a spreadsheet. These numerical vectors are much easier to work with mathematically and can be used to develop classification models, entity extraction models, question and answering – the list goes on and on.
Q: How do you identify which problem area(s) to start with for your data analytics and cognitive technology projects?
Rick McFarland: In the professional-grade AI space that we work in, i.e. Law and Medicine, we have a unique challenge that developers for consumer-grade AI don’t necessarily have. In our space, the education distance between the developer and the customer is wide. For example, a lawyer (our customer) has gone to school for many years including post graduate work to learn a very advanced and specific skill and, one would argue, even mastered a new language (if you’ve ever read a legal document, you know what I am talking about!) Similarly, Data Scientists also have spent a long time honing their skills and often have a PhD in computer science, and also have learned a programming language or two. With these two professions on different ends of the educational spectrum, communication and business understanding between the two are often difficult. Because of this, the data science and development teams at LN partner very closely with our Product Team. Our Product Team, some of whom are lawyers, stay closely connected to our customers and perform frequent surveys and focus groups to understand their needs. They are also experts at working with developers and data scientists. These three job families (product, developer and data scientist/analyst make up our standard team. We rely on this close connection to keep us on top of the main problem areas of our clients.
Q: What are some of the unique opportunities you have when it comes to data and AI?
Rick McFarland: AI is only as good as the data used to train and feed it. And LexisNexis has one of the largest repositories of legal data in the world. But, as any data scientists will tell you, having raw data is half the battle. What makes our data special is that, since the invention of the computer, we have had thousands of lawyers on staff enriching, summarizing, identifying entities, mapping citations, etc., on this massive corpus. Therefore, we also have the other half of the data scientists’ requirement: we have petabytes of training data. We have all the raw building materials for AI development. Our data scientists have almost unlimited opportunity to create AI products and features. We can take nearly any “AI idea” from concept to POC in a matter of weeks!
Q: Can you share some of the challenges when it comes to AI and ML adoption?
Rick McFarland: In the professional world (e.g., Legal, Medical, and Scientific), the bar for AI and ML is very high, so the adoption rate is much lower than in the consumer market. In these professions, where people’s lives or freedoms are at stake, the cost of being wrong has significant consequences. For these professionals to rely on an AI application means that it must perform well and must be accurate consistently. With one wrong answer, trust in that tool is breached – recovery is long and perhaps not even possible. For LexisNexis to release any AI to the professional marketplace, it must meet that high bar.
We do something unique in the professional-grade space, something consumer-grade technology providers probably don’t do. Most AI developers are familiar with the “holdout” sample method to evaluate the quality of a model – the holdout data is randomly selected from the training data, hidden from the developers, not used as part of the training process, and used to evaluate the final model. In the professional-grade process, we also maintain another holdout data set which we refer to as the Platinum Data. In our Q&A process, this is a set of questions that the current model correctly answers. In fact, these answers are rated as the “best” by our SME’s and customers. With every new model we release, we re-check the answers generated by our system on the Platinum questions. If the answers provided are not the same (or worse), we don’t release the model into production. As you might guess, with each new release this Platinum Data grows. We are not only providing correct answers – we are ensuring that we are providing consistency, which is very important in the Legal industry where court cases can sometimes go on for months or even years.
Q: How do analytics, automation, and AI work together at LexisNexis?
Rick McFarland: At LexisNexis, a visualization of how these three functions operate can be represented by a three-way Venn Diagram. There are situations when Analytics can be applied independently, or in combination with AI, or with Automation, or with both. It’s true with each of these three. We have team leaders running each of these functions, each with deep skills in their area, and they also frequently work together on projects. For example, it’s not uncommon for the Automation team to partner with the AI team to develop a chatbot for our Customer Support team to help reduce the need for human support. Similarly, the Analytics team will work with the AI team to develop a cross-sell model that could then be deployed on an Analytics dashboard to help make recommendations to support our Sales Teams.
Q: How are you navigating privacy, trust, and security concerns around the use of your data?
Rick McFarland: Most of our data is from publicly available sources (i.e., case law, dockets, etc.), and we tap into more than 50,000 sources. In addition, we digitize and standardize it to make it easily readable and searchable. Attorneys use the data to serve their clients, and we view our role as making the Law accessible and readable. This supports our global vision of making the world a more just place by advancing the Rule of Law.
Q: What are you doing to develop a data-literate and AI-ready workforce?
Rick McFarland: One of the things we are especially proud of at LexisNexis is our Advanced Technical Curriculum. We learned early on when we started hiring Data Scientists that most are well trained at consumer-grade AI development. That’s what is taught in schools and training courses because there’s a lot of data and examples out there. What Data Scientist hasn’t made at least one model from Twitter data today? So, we created the ATC as a sort of “grad school” for Data Scientists to learn how to develop professional-grade AI.
This curriculum, like in most universities, has a syllabus of classes with levels ranging from 100-level to 400-level. Our 100-level classes are accessible to all, and we focus these on the platforms and tools that are essential to AI development. We have AWS and Azure training because we learned that most data scientists enter the door with a different knowledge of how to use these core platforms, and we needed our people to learn how to use these platforms while understanding the professional-grade requirements set by our clients. Our 200-level classes focus on domain-specific and core languages like Python. Our 300-level courses are designed to focus on AI methods. Since we primarily deal with Legal and Textual (non-structured) data, we offer classes in Text Processing, Machine Learning, Natural Language Processing (NLP), and contextualized embeddings (e.g., BERT). Our 400-level courses are where we pull it all together and focus on the application of these advanced AI building-blocks to develop AI features and products. So, for example, these courses teach how to build professional-grade chatbots, recommender systems, learning-to-rank models, etc. Once our internal students have completed their Advanced Tech Curriculum Education, they will earn their LexisNexis Profession-Grade Data Science Certification and be on their way to developing the next Legal AI tool.
Q: What AI technologies are you most looking forward to in the coming years?
Rick McFarland: What I am really looking forward to is the true Star Trek-like “voice intelligence” that can answer any question we ask. It will understand colloquialisms and idioms. It will easily shift from answering generic, everyday questions to answering complex scientific and professional questions. It also will understand the context of questions – it would be able to distinguish between me talking about Coach “the person” or Coach the “brand.” The true leap will come when this voice-intelligence anticipates (or even predicts) what my ultimate end goal is based on my question and context and gives me some additional information that I may not have asked for to help me achieve my goal. That will be truly exciting.
Hear more from Rick McFarland at the upcoming Data for AI virtual event on August 19.
Published at Sun, 25 Jul 2021 14:15:00 +0000